The chest x-ray is widely used for detecting a number of patient conditions and for imaging a range of skeletal and organ structures. Radiographic images of the chest are useful for detection of lung nodules and other features that indicate lung cancer and other pathologic structures and other life-threatening conditions.
The chest region includes a wide range of tissues, ranging from rib and other bone structures to the lung parenchyma. This greatly complicates the task of radiographic imaging for the chest region, since the different types of bone and tissue materials have widely different densities. Optimization techniques for chest imaging require making a number of compromises to provide a suitable signal-to-noise (S/N) ratio and sufficient contrast for soft tissue.
Given this complexity of image content, the challenging task of forming a volume image of the chest using tomography is even more formidable. Due to factors such as beam hardening, rib edges, and various other features of the image content that is obtained and used to form the volume image, various types of artifacts are often generated, obscuring image content of interest and rendering the chest volume image less useful to the practitioner. One type of artifact that is characteristic of the tomography chest volume image is a ripple artifact that is caused by rib edges. When the projection image data is processed to form the tomography volume image, ripple artifacts are often visible in the rendered volume image and can be difficult to correct or minimize.
Due to the limited sweep angle used in tomosynthesis imaging, the data acquired is not sufficient to accurately reconstruct the scanned object. As a result of some amount of missing data, the tomosynthesis reconstruction can produce numerous artifacts in subsequent reconstruction of volume image content. In particular, high contrast objects produce ripple artifacts in the in-plane slice images (streak artifacts in the depth images) of tomosynthesis. These artifacts are due to the incomplete cancellation of objects that are spatially located outside the reconstructed image plane. The ripple artifact can be suppressed by increasing the projection density, the number of projection images acquired divided by the scan angle. Or, alternatively, the ripple artifact can be suppressed by low pass filtering the reconstruction; however, this can result in a blurred reconstruction, limiting its diagnostic utility. Thus, there remains a need for a method for reducing ripple artifacts to increase the diagnostic quality of the reconstructed images, with a lower number of projection images translating into lower dose exams and shorter scan times than are used for other volume imaging modalities.
In 2-D radiological imaging, various methods have been proposed and used for detecting and suppressing rib structures and allowing the radiologist to view the lung fields without perceptible obstruction by the ribs. Some methods have used template matching or rib edge detection and curve fitting edge detection.
Among other solutions that have been proposed for rib suppression, US 2009/0290779 entitled “Feature-based neural network regression for feature suppression” by Knapp describes the use of a trained system for predicting rib components and subsequently subtracting the predicted rib components. US 2009/0060366 entitled “Object segmentation in images” by Worrell describes techniques using detected rib edge to identify rib structures.
An article entitled “Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive Training Artificial Neural Network (MTANN)” by Suzuki et al. in IEEE Transactions on Medical Imaging, Vol. 25 No. 4, April 2006 describes methods for detection of lung nodules and other features using learned results from a database to optimize rib suppression for individual patient images. The MTANN approach described above has limitations, however, as it requires dual energy images as part of the training database. Further, MTANN may not be able to accurately estimate the edge of the bone as well as it estimates the bone density elsewhere. As a result, further work on the bone edge suppression is required.
An article entitled “Detection and Compensation of Rib Structures in Chest Radiographs for Diagnose Assistance” in Proceedings of SPIE, 3338:774-785 (1998) by Vogelsang et al. describes methods for compensating for rib structures in a radiographic image. Among techniques described in the Vogelsang et al. article are template matching and generation and selection from candidate parabolas for tracing rib edges.
An article entitled “Model based analysis of chest radiographs”, in Proceedings of SPIE 3979, 1040 (2000), also by Vogelsang et al. describes Bezier curve matching to find rib edges in a chest radiograph for alignment of a model and subsequent rib shadow compensation. Interpolation and a compensation mask are employed in this method.
While some of these 2-D methods may have achieved a level of success for suppression of rib structure using rib edge detection approaches to identify rib structures, improvements can be made. For example, effort is needed to adapt the rib detection method to individual patient images, as template or function-fitting of the detected rib edge methods have limitations for handling large variations in the shape of ribs and image quality. This can be more difficult when foreign objects, e.g., tubes/lines and other devices, are captured in ICU portable chest images.
With many of these methods, non-zero density estimation in non-rib areas could contribute to added noise in these areas, which will affect the overall image quality of the rib suppressed images. The rib detection methods used have generally been memory-intensive, requiring significant computational resources. Robustness is also desirable. Even if rib structures are well-defined, however, it can be challenging to remove rib features from the chest x-ray image without degrading the underlying image content that can include lung tissue. There is a need for a method of rib suppression which accurately detects the ribs including clavicles in chest x-ray images and suppresses the rib area only, while preserving the image content of underlying lung tissue. This is of particular utility for generating tomography volume images from a set of 2-D projection images. Each 2-D projection image can be individually processed to suppress and remove rib content. However, a number of the methods described previously provide disappointing results for tomographic imaging when such processed images are combined.
Thus, it can be seen that there is a need for improved methods for generating tomography volume images having suppressed rib content.